Lectin Microarray-based Glycomics and Machine Learning Identify Shared Osteoarthritis Biomarkers in Humans, Dogs, and Horses
Bibliographic record
Abstract
Abstract Post-traumatic osteoarthritis (PTOA) is a common sequela to joint injury in both humans and companion animal species such as horses and dogs. Despite the increasing prevalence of osteoarthritis (OA) in humans, investigation of glycosylation changes associated with OA remains in its infancy. Recent advances, such as lectin microarray analysis, now enable detailed glycan profiling in complex biofluids such as synovial fluid. Using lectin microarray technology, this study characterized glycosylation patterns in synovial fluid samples from healthy and OA-affected joints in horses, dogs, and humans. Comparative glycan-binding profiles within and between species revealed conserved and distinct glycomic signatures associated with OA. Machine learning models, including classification algorithms, effectively distinguished OA from healthy joints, identifying key lectins and glycan epitopes crucial to these predictions. The identified lectin markers reflect specific glycosylation pathways and potential inflammatory mechanisms, demonstrating their value in differentiating between healthy and OA phenotypes. Our findings underscore the promise of integrated glycomic profiling and machine learning to enhance our understanding of glycan involvement in the pathogenesis of OA and to facilitate the development of diagnostic and therapeutic strategies applicable to both veterinary and human medicine. In Brief Osteoarthritis affects humans and companion animals; however, its molecular features remain unclear. Using lectin microarrays and machine learning, we identified conserved and species-specific glycan signatures in synovial fluid that differentiate between control and osteoarthritic joints. This One Health approach highlights shared molecular mechanisms of joint degeneration and establishes data-driven glycomic profiling as a framework for understanding osteoarthritis across species. Graphical Abstract
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".